The Strategy of Mining Association Rule Based on Cloud Computing

TLDR

Cloud computing offers inexpensive and efficient storage and analysis of large datasets. The paper focuses on developing a parallel association rule mining strategy for cloud computing environments. The authors design a parallel association rule mining strategy on Hadoop using MapReduce, incorporating data partitioning, allocation, an improved Apriori algorithm, and implement it on the Hadoop platform. The strategy achieves higher efficiency for frequent item set mining in cloud computing environments.

Abstract

Cloud computing provides cheap and efficient solutions of storing and analyzing mass data. It is very important to research the data mining strategy based on cloud computing from the theoretical view and practical view. In this paper, the strategy of mining association rules in cloud computing environment is focused on. Firstly, cloud computing, Hadoop, MapReduce programming model, Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a parallel association rule mining strategy adapting to the cloud computing environment is designed. It includes data set division method, data set allocation method, improved Apriori algorithm, and the implementation procedure of the improved Apriori algorithm on MapReduce. Finally, the Hadoop platform is built and the experiment for testing performance of the strategy as well as the improved algorithm has been done. The results show that the strategy designed in this paper can archive higher efficiency when doing frequent item set mining in cloud computing environment.